Symmetries in human brain language pathways
correlate with verbal recall
Marco Catani*†‡§
, Matthew P. G. Allin†
, Masud Husain¶
, Luca Pugliese*, Marsel M. Mesulamʈ
, Robin M. Murray†
,
and Derek K. Jones**
*Section of Brain Maturation, †Department of Psychological Medicine, and ‡Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College
London, London SE5 8AF, United Kingdom; ¶Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom;
ʈNorthwestern University, 320 East Superior Street, 11-450, Chicago, IL 60611; and **Cardiff University Brain Research Imaging Centre, School of
Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom
Edited by Fernando Nottebohm, The Rockefeller University, Millbrook, NY, and approved September 7, 2007 (received for review March 7, 2007)
Lateralization of language to the left hemisphere is considered a
key aspect of human brain organization. We used diffusion tensor
MRI to perform in vivo virtual dissection of language pathways to
assess the relationship between brain asymmetry and cognitive
performance in the normal population. Our findings suggest interhemispheric
differences in direct connections between Broca’s
and Wernicke’s territories, with extreme leftward lateralization in
more than half of the subjects and bilateral symmetrical distribution
in only 17.5% of the subjects. Importantly, individuals with
more symmetric patterns of connections are better overall at
remembering words using semantic association. Moreover, preliminary
analysis suggests females are more likely to have a
symmetrical pattern of connections. These findings suggest that
the degree of lateralization of perisylvian pathways is heterogeneous
in the normal population and, paradoxically, bilateral representation,
not extreme lateralization, might ultimately be advantageous
for specific cognitive functions.
arcuate fasciculus ͉ brain asymmetry ͉ diffusion tensor imaging ͉
language lateralization ͉ verbal memory
One of the most distinctive features of the human brain is the
asymmetric distribution of cognitive functions between the
two hemispheres, generally referred to as lateralization (1).
Although the behavioral correlates of structural asymmetry
described in other animals remain to be clarified (2, 3), in
humans, left–right differences have been linked to the development
of language (4). Evidence for specialization of the human
left hemisphere for language derives from lesion, functional, and
structural anatomical findings (1, 4–9).
The most compelling evidence for language lateralization
comes from studies of patients with language deficits after brain
lesions. In right-handed adults presenting with aphasia, the brain
lesion is almost invariably located in the left hemisphere (1, 5).
Similar prevalence data have been found in subjects undergoing
brain surgery for epilepsy, with lateralization of language to the
left hemisphere being observed in Ͼ90%, but not all, of the
right-handed subjects (7, 9, 10). The structural correlates of
language lateralization are less clear. In their original paper,
Geschwind and Levitsky (4) found an asymmetry of the size of
the planum temporale, a region of the temporal lobe belonging
to Wernicke’s territory, in 65% of the population. However,
Dorsaint-Pierre et al. (10) have recently found that the leftward
lateralization of the planum temporale does not correlate with
language lateralization as assessed by the sodium amytal procedure.
On the other hand, several studies have reported a higher
prevalence of leftward asymmetry of perisylvian white matter
volumes, suggesting that language networks represent a more
likely anatomical substrate for lateralization of language functions
rather than cortical areas alone (11–14). However, the
behavioral correlates of lateralization of white matter pathways
remain unknown.
Despite this extensive research, it remains unclear whether
language abilities actually relate to the degree of brain asymmetry
(7, 10). Indeed, recent studies show that the right hemisphere
may contribute to some aspects of language function,
such as semantic processing (15, 16), word learning (17), and
retrieval (18, 19).
Here we used diffusion tensor (DT) MRI (DT-MRI) tractography
to examine the asymmetry of perisylvian language pathways
and behavioral correlates. DT-MRI tractography is a
noninvasive technique that, instead of visualizing axons directly,
reconstructs their trajectories by measuring the diffusivity of
water along different directions and tracing a pathway of least
hindrance to diffusion (parallel to the dominant fiber orientation)
to form continuous pathways. DT-MRI facilitates the study
of large-scale brain networks, including those subserving language
(11–13, 20–22). Recent DT-MRI investigations have
revealed three important parts of the language network (21).
First, there is the well established direct pathway (21) connecting
Wernicke’s territory in the left temporal lobe with Broca’s
territory in the left frontal lobe through the arcuate fasciculus
(11–13, 20–22). In addition, there is an indirect pathway (21)
consisting of two segments, an anterior segment linking Broca’s
territory with the inferior parietal lobe (Geschwind’s territory)
(21), and a posterior segment linking Geschwind’s territory with
Wernicke’s territory (13, 20, 21). Not only does this arrangement
support the more flexible architecture of parallel processing
(23), but it is also in keeping with recent functional neuroimaging
findings (8, 16, 24) and contemporary models of language
disorders (5, 23, 25). The current study focused on the lateralization
of these pathways and possible behavioral advantages
related to the degree of lateralization.
Results
First, we obtained DT-MRI brain scans of 50 healthy righthanded
subjects (30 males and 20 females) with a 1.5-T GE
scanner, using an acquisition sequence fully optimized for DTMRI
of white matter and coverage of the whole head (26). Then
we adopted a two-step approach to brain dissections. The first
step consisted of creating an average template from DT-MRI
data sets of 10 male subjects as described in Jones et al. (27). In
Author contributions: M.C., M.P.G.A., M.H., M.M.M., and R.M.M. designed research; M.C.,
M.P.G.A., L.P., and D.K.J. performed research; M.C., M.P.G.A., L.P., and D.K.J. contributed
new reagents/analytic tools; M.C., M.H., L.P., and D.K.J. analyzed data; and M.C., M.P.G.A.,
M.H., L.P., M.M.M., R.M.M., and D.K.J. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Abbreviations: DT, diffusion tensor; CVLT, California Verbal Learning Test; ROI, region of
interest.
§To whom correspondence should be addressed. E-mail: m.catani@iop.kcl.ac.uk.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0702116104/DC1.
© 2007 by The National Academy of Sciences of the USA
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each hemisphere, virtual dissections of the three segments of the
perisylvian language networks were performed for the average
brain (21). Then, to confirm the exploratory analysis derived
from dissections of the average brain, similar dissections were
performed in each of the remaining 40 single subjects.
Asymmetry of Perisylvian Language Networks. The reconstruction
of the perisylvian language networks in the average DT-MRI
data set indicates an asymmetrical representation, with a very
different connection pattern in the left hemisphere compared
with the right (Fig. 1A). Critically, although the indirect pathway
is represented in both hemispheres, consisting of an anterior
indirect segment (denoted in green in Fig. 1A) and a posterior
indirect segment (denoted in yellow in Fig. 1A), it was not
possible to reconstruct pathways directly connecting frontal and
temporal regions in the right hemisphere of the average data set.
Only the left hemisphere reconstructions contain a direct segment
(denoted in red in Fig. 1A).
Although these findings suggest differences in language networks
between the left and right hemispheres, they were derived
from an averaged DT-MRI data set. Of critical importance is
whether such asymmetries are observed in individual subjects.
We therefore performed individual dissections on the data
acquired from 40 subjects and created, for each segment, maps
showing the overlap of the segment from each subject. The
segment overlap maps derived from the single brain dissections
confirm the asymmetrical distribution of the direct segment (Fig.
1B) and the symmetrical distribution of the anterior indirect
(Fig. 1C) and posterior indirect (Fig. 1D) segments. [The complete
series of the segment overlap maps can be seen in supporting
information (SI) Fig. 3.]
The segment-overlap maps give an overall idea of the interhemispheric
distribution of the number of reconstructed pathways
but may underestimate the heterogeneity within the sample
studied. For this reason, a lateralization index was calculated by
counting the number of reconstructed pathways within the direct
segment for each hemisphere. To facilitate a visual representation
of the heterogeneous distribution, a k-means cluster analysis
was performed to broadly classify the data sets into three groups
(Fig. 2A). This procedure makes no assumptions about underlying
differences between individuals but attempts to objectively
identify relatively homogeneous groups of cases. The cluster
analysis showed that 62.5% (25/40) of the subjects presented an
extreme left lateralization of the direct segment (cluster center
of the lateralization index ϭ 1.93) (Group 1). In the majority of
subjects in Group 1, it was not possible to reconstruct a
continuous trajectory between equivalents of Broca’s and Wernicke’s
territories in the right hemisphere. Another 20% (8/40)
of all of the subjects demonstrated leftward asymmetries but
with some representation of the right direct segment in the
reconstructed pathways (cluster center of the lateralization
index ϭ 1.03); thus they had a bilateral but leftward asymmetric
distribution (Group 2). Only 17.5% (7/40) of the subjects had
similar left-right distribution (cluster center of the lateralization
index ϭ 0.03) (Group 3).
Further, for each subject, we obtained the mean value of the
fractional anisotropy sampled along each tract segment, which we
term ‘‘tract-specific measurement’’ (28). Fractional anisotropy is a
scalar measure that reflects the degree to which the diffusivity
depends on the orientation in which it is measured (changes in
which are thought to reflect changes in tissue microstructure) and
therefore considered an index of microstructural ordering and
integrity of fibers. Interhemispheric differences were found in the
fractional anisotropy, with values of the anterior indirect segment
(left, 0.445 Ϯ 0.024; right, 0.465 Ϯ 0.023; P ϭ 0.001) and, to a lesser
degree, the direct segment (left, 0.488 Ϯ 0.024; right, 0.475 Ϯ 0.029;
P ϭ 0.048) being significantly different between the left and right
sides. Also, for each subject and segment, a lateralization index of
the fractional anisotropy values was calculated (SI Fig. 4). A
rightward distribution of the fractional anisotropy lateralization
index of the anterior indirect segment (mean ratio ϭ Ϫ0.03 Ϯ 0.64;
P ϭ 0.002) and a leftward distribution of the fractional anisotropy
index of the direct segment (mean ratio 0.03 Ϯ 0.073; P ϭ 0.035)
were found.
Patterns of Lateralization and Behavioral Correlates. A significant
positive correlation was found between the lateralization index of
reconstructed pathways in the direct segment and the performances
in the California Verbal Learning Test (CVLT; total words recall)
(Pearson’s correlation 0.564, P Ͻ 0.001; correlations are significant
at P Ͻ 0.005 after Bonferroni correction) (Fig. 2B). The CVLT
consists of five learning trials of a 16-word list. The list is read aloud
by the examiner, and the examinee is instructed to freely recall as
many words as possible, in any order. Each of the 16 words belongs
to one of four categories of ‘‘shopping list’’ items (i.e., fruits, herbs
and spices, articles of clothing and of tools). The idea underlying the
Fig. 1. Left–right hemispheric differences in perisylvian language pathways. (A) Tractography reconstruction of language pathways in the average DT-MRI data
set. In both hemispheres, the anterior indirect segment (green) connects posterior inferior frontal cortex (B, Broca’s territory) and inferior parietal cortex (G,
Geschwind’s territory), whereas the posterior indirect segment (yellow) connects superior posterior temporal cortex (W, Wernicke’s territory) and Geschwind’s
territory. In the left hemisphere, but not in the right, the direct segment (red) connects Wernicke’s and Broca’s territories. (B–D) Segment overlap maps derived
from dissection of single brains show L-R asymmetry only in the direct segment (B) and L-R symmetry in the anterior indirect (C) and posterior indirect (D)
segments. ‘‘Hotter’’ (yellow) colors correspond to a higher degree of overlap, whereas ‘‘cooler’’ (red) colors reflect less overlap.
17164 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0702116104 Catani et al.
CVLT is that lists of words are easier to remember if they are
broken down by using a strategy of grouping them into semantic
categories. After the first trial, the same 16-word list is reread aloud
by the examiner, and the examinee is asked to recall again as many
words as possible. The same procedure is used for the remaining
three trials. Overall, total recall (i.e., the number of words correctly
recalled of 80) was lower for subjects with extreme left lateralization
(Group 1) (53.5 Ϯ 6) than for those with bilateral, symmetrical
(Group 3) (68.2 Ϯ 5.3; P Ͻ 0.001), or asymmetrical patterns (Group
2) (61.7 Ϯ 7; P Ͻ 0.01) (Fig. 2C). Thus, a more symmetrical pattern
was associated with better performance on the CVLT. There were
no significant differences among the three groups in performance
on any of the other neuropsychological tests performed.
Gender Differences in the Lateralization Pattern. A preliminary
analysis was performed to assess gender differences in the
anatomy of perisylvian language pathways. Fig. 2D shows the
difference in the distribution of lateralization pattern between
the two genders (Fisher’s exact test ϭ 8.496; P ϭ 0.018). Overall,
males showed a greater leftward lateralization (mean lateralization
index ϭ 1.52 Ϯ 0.64) compared with females (mean
lateralization index ϭ 1.07 Ϯ 0.72) (P Ͻ 0.05). Despite the
anatomical differences, both genders showed a significant correlation
between the pattern of lateralization of the direct
segment and the performances in the CVLT (Fig. 2E). Thus in
males, Group 1 performed worse than Group 2 (P Ͻ 0.05); in
females, Group1 performed worse than Group 2 (P Ͻ 0.05) and
Group 3 (P Ͻ 0.01).
There were no differences between the genders (SI Table 1)
in the lateralization index of the fractional anisotropy values and
no correlation between the fractional anisotropy values and
neuropsychological performances after correction for multiple
comparisons (SI Table 2).
Discussion
DT-MRI tractography is the only technique that allows the
identification of large pathways and assessment of microstructural
integrity of white matter in the living human brain (29).
Hence, we used DT-MRI tractography to look for correlation
between symmetries within white matter of the language system
and behavioral performances.
Previous studies have shown direct correspondence between
tractography-derived anatomy of white matter pathways and
classical postmortem descriptions (30, 31). Nevertheless, it is
important to note that DT-MRI tractography offers, at best, only
indirect indices of tissue properties, and therefore there is
uncertainty in the correspondence between tractography measurements
(e.g., number of reconstructed pathways) and underlying
biological factors (e.g., number of axons, degree of myelination,
etc.). We therefore do not assume that our findings are
Fig. 2. Lateralization of language pathways and behavioral correlates. (A) Distribution of the lateralization pattern of the direct long segment (red). (B)
Significant correlation between the lateralization index (streamlines) of the direct segment and performances on the CVLT (number of words correctly recalled
of 80). (C) Performances in the CVLT according to the three lateralization groups (*, P Ͻ 0.01 vs. Group 1; †, P Ͻ 0.001 vs. Group 1). (D) Distribution of the
lateralization groups between genders. (E) Performances in the CVLT according to the lateralization pattern and gender (striped colors are females) (*, P Ͻ 0.05
vs. Group 1; †, P Ͻ 0.01 vs. Group 1; ‡, P Ͻ 0.05 vs. Group 1).
Catani et al. PNAS ͉ October 23, 2007 ͉ vol. 104 ͉ no. 43 ͉ 17165
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equivalent to data obtained from postmortem dissections, although
they are likely to reflect highly reproducible features of
the human brain anatomy.
Converging evidence of the existence of the three perisylvian
segments derives from human intraoperative electrocorticography
(32), functional connectivity (33), and postmortem dissections
(34), and there is also some evidence of a rudimentary
homologue of the three segments in the monkey (35). However,
none of the previous studies were concerned with interhemispheric
differences and the correlation between lateralization of
pathways and language function.
Three important findings emerge from this study. First, the
white matter anatomy of perisylvian language networks differs
between the two hemispheres. Second, the pattern of lateralization
is heterogeneous in the normal population. Finally,
bilateral representation of language networks is associated with
better performance in learning words by semantic association.
The overall prevalence of asymmetrical distribution of the
direct segment of the arcuate fasciculus in our right-handed
population is higher (82.5%) than that reported for the planum
temporale (65%), the region of the posterior superior temporal
gyrus classically associated with language lateralization (4). Considering
that the prevalence of left functional ‘‘dominance’’ for
language is Ͼ90% (9, 10), asymmetry of the direct segment may
represent a key anatomical substrate for language lateralization.
In general, our findings are consistent with previous neuroimaging
studies showing structural (11, 12, 22, 36, 37) and
maturational (38) differences of the arcuate fasciculus between
left and right hemispheres. However, the existence of an arcuate
fasciculus in the right human brain has never been confirmed in
postmortem studies and, to the best of our knowledge, such a
strong relationship between brain pathway architecture and
cognitive performance has never previously been described. The
CVLT assesses encoding and retrieval of a list of auditorily
presented words. Because each word in the list can be categorized
in one of the four ‘‘shopping list’’ groups and can therefore
be clustered together with other semantically associated words,
the CVLT is considered a test that does not examine verbal
memory in itself, but rather some level of interaction between
verbal memory and conceptual ability (39). The CVLT is a
complex cognitive task that is known to require additional
involvement of the right hemisphere as shown by fMRI in
subjects performing the test (17). Our data suggest that direct
connections between inferior frontal and posterior temporal
regions in the right hemisphere confer an advantage for the
performance of this task. This finding is supportive of the role
of the right hemisphere in word retrieval (40) and in keeping with
evolutionary theories of the effects of brain lateralization on
human cognition (41). On the other hand, we did not find
correlations with other language functions that were tested in the
study group. It is possible that some of the linguistic tasks we
have tested may correlate with other tracts that were not
analyzed in our study or alternatively may be not specific to any
single anatomical structure. Verbal fluency, for example, is associated
with lesions of an extended network connecting lateral to
medial frontal cortex and the head of caudate, a network that is not
part of the perisylvian circuitry (5).
A preliminary analysis of the data also suggests gender
differences on the large-scale anatomy of language pathways but
no differences at the microstructural level (i.e., males and
females had similar lateralization indices of fractional anisotropy).
Some studies have shown gender differences in language
cortical (42) and subcortical anatomy (11), activation patterns on
linguistic tasks (43), and cognitive performances (44), whereas
other studies showed no differences (45). Here, we found that
the female group had lower lateralization of the direct segment
and slightly better performances in the CVLT compared with
males (SI Table 3). Kramer et al. (46) have demonstrated that
females tend to outperform males on recall measures of this test.
However, our findings of better performances of Group 2 over
Group 1 in both genders (Fig. 2E) support the notion that the
main determinant of better performance is the anatomy (i.e.,
symmetry) of the language pathways, not the gender.
In conclusion, our data suggest the relationship between
functional and anatomical lateralization might not be as straightforward
as previously believed. Our findings not only show
hemispheric differences in connections between inferior frontal
and posterior temporal regions but also critically suggest that
individuals with more symmetric patterns of connection are
better overall at learning words using semantic association. The
correlation between anatomical lateralization and neuropsychological
performances brings up interesting hypotheses on possible
anatomical predictors of aphasia recovery that can be tested
in patients with lesions of perisylvian language networks.
Experimental Procedures
Study Participants. Data sets from 10 male subjects (mean age ϭ
33.3 Ϯ 4.7 years) were used to create an average tensor data set
as described in Jones et al. (27) (see below). Data sets from 40
additional subjects (20 males and 20 females, age range 18–22
years) were used to perform single dissections of the three
language segments and create segment overlap maps. All subjects
were right-handed healthy individuals. Approval was obtained
from the Joint Medical Ethical Committee of the Institute
of Psychiatry, Kings College London. Informed written consent
was obtained from all participants.
Neuropsychological Testing. The neuropsychological testing (SI
Table 3) included: the Wechsler Abbreviated Scale of Intelligence;
block design and matrices as subtests for performance IQ
and vocabulary and similarities for performance IQ (47); the
Controlled Oral Word Association Test (to test phonological and
semantic verbal fluency) (48); the Hayling Sentence Completion
Task (a test of verbal initiation and inhibition) (49); the CVLT
(to assess the use of semantic associations as strategy for learning
words) (50); and the Wechsler Memory Scale (immediate and
delayed recall of geometric shapes) (51). Handedness was assessed
by using the Edinburgh Handedness Inventory (52).
DT-MRI Acquisition. Data were acquired on a GE Signa 1.5-T LX
MRI system (General Electric, Milwaukee, WI) with 40-mT/m
gradients, using an acquisition sequence fully optimized for
DT-MRI of white matter, providing isotropic resolution (2.5 ϫ
2.5 ϫ 2.5 mm) and coverage of the whole head. The acquisition
was gated to the cardiac cycle by using a peripheral gating device
placed on the subjects’ forefinger. Full details of the acquisition
sequence are provided in Jones et al. (26).
After correction for the image distortions introduced by the
application of the diffusion encoding gradients, the DT was determined
in each voxel following the method of Basser et al. (53). After
diagonalization of the DT, the fractional anisotropy (54) was
measured in each voxel. To ensure that the observer was blind to
hemisphere during the virtual dissection of the language pathways
and therefore to provide protection against subjective bias, half of
the DT-MRI data sets were reflected about the midline.
Averaging of DT-MRI Data Sets. DT-MRI data sets from 10 male
subjects were spatially normalized to the anatomical reference
space defined by the Montreal Neurological Institute Echo
Planar Imaging (MNI EPI) template supplied as part of the of
the functional imaging analysis software package SPM99 (statistical
parametric mapping; Wellcome Department of Cognitive
Neurology, London, U.K.). The use of the male average brain
was preferred for consistency with our previous findings, where
we described the three segments for the first time in the left
hemisphere of 10 male subjects (21). For each of the 10 source
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subjects, the masked tensor volume data set was coregistered
(using an affine registration with 12 degrees of freedom) to the
template using the approach described by Alexander et al. (55).
This approach uses the Automated Image Registration (AIR)
registration package (56, 57) for coregistration, and the computed
transformations thus obtained are applied to the DT-MRI
volumes using the preservation of principal directions algorithm
(55), which has been shown to reorient each tensor correctly
under nonrigid transformations. The images used for coregistration
were the fractional anisotropy images computed from the six
elements of the tensor (54). Full details of the spatial normalization
procedure and generation of an average DT-MRI volume are
described in Jones et al. (27).
Tractography Algorithm. The software for reconstructing the
trajectories of tracts from DT data were written in the ‘‘C’’
programming language and based on the procedure originally
described by Basser et al. (58). Details of the method have been
published (21, 30), but a brief description will follow. First, a
continuous description of the DT field was derived from the
voxel-wise discrete estimates by B-spline fitting a series of basis
functions to the elements of the tensor matrices (58). This
procedure allows rapid evaluation of the DT at any arbitrary
location within the imaged volume and also permits smoothing
of the tensor field. A set of locations for the initiation of the
tracking algorithm (the ‘‘seed-points’’) was first selected on the
fractional anisotropy images (see below). For each of these
seed-points, the DT was estimated and diagonalized to determine
the principal eigenvector. The tracking algorithm then
moved a distance of 0.5 mm along this direction. The DT was
determined at this new location (obtained from the continuous
description of the tensor field) and the orientation of its principal
eigenvector estimated. The algorithm then moved a further 0.5
mm along this new direction. A pathway was traced out in this
manner until the fractional anisotropy of the tensor fell below a
fixed arbitrary threshold (set to 0.2). The procedure was then
repeated by tracking in the opposite direction, to reconstruct the
whole tract passing through the seed-point.
At the termination of tracking, the number of reconstructed
pathways and the fractional anisotropy, which quantifies the
directionality of diffusion on a scale from zero (when diffusion
is totally random) to one (when water molecules are able to
diffuse along one direction only), was sampled at regular (0.5mm)
intervals along the tract and the means computed (28). For
each reconstructed segment, a lateralization index was calculated
according to the following formula (N., number):
͑N. streamlines-left͒ Ϫ ͑N. streamlines-right͒
͓͑N. streamlines-left͒ ϩ ͑N. streamlines-right͔͒/2.
Positive values of the index indicate a greater number of
streamlines in the left direct segment compared with the right.
Values around the zero indicate a similar number of streamlines
between left and right. Similarly, a lateralization index was
calculated for the fractional anisotropy values of each segment.
Virtual Dissections of the Language Networks and Segment Overlap
Maps. SI Fig. 5 shows the method used to perform virtual
dissections of the left and right perisylvian connections between
frontal, parietal, and temporal regions (21). A region of interest
(ROI) was defined on the axial fractional anisotropy map
computed from the average DT-MRI volume, to encompass the
horizontal fibers lateral to the corona radiata and medial to the
cortex extending from Talairach z ϭ 22 to z ϭ 28. All fibers
passing through this ROI were reconstructed in three dimensions
by using MATLAB (Mathworks, Natick, MA) and visualized
as illuminated streamtubes. The ROI was defined on axial
slices as this projection facilitates the visualization of the borders
between the fibers of the arcuate and those of the corona radiata.
A two-ROI approach was used to perform further detailed
dissection of the arcuate fasciculus, allowing us to separate
different sets of fibers within the arcuate bundle. Here, two
spatially separated regions are defined in the fractional anisotropy
volume, and all fibers passing through both are visualized
as described above. The approach does not constrain tracts to
start and end within the defined regions, only to pass through
them. The distribution of arcuate fiber terminations found by
tractography extends beyond the classical limits of Broca’s and
Wernicke’s areas to include in addition to the inferior frontal
cortex part of the middle frontal gyrus and in addition to the
superior temporal cortex the posterior middle temporal gyrus
respectively. For this reason, we refer to these extended regions
as Broca’s and Wernicke’s territories (21). The delineation of
regions used in two-ROI dissections was guided by the results of
the one-ROI dissection. The single segments were visually
inspected for the presence of aberrant streamlines (e.g., streamlines
leaving the arcuate fasciculus and projecting into the
internal capsule) and anatomical correspondence between the
two hemispheres. The number of seeds use to start tracking was
similar between the two hemispheres, and none of the subjects
showed aberrant fibers within the direct segment when the
two-ROI approach was used.
For each subject and segment, a binary map with dimensions
equal to that of the DT-MRI data (i.e., 128 ϫ 128 ϫ 60) was
computed by assigning each pixel a value of 1 or 0 depending on
whether the pixel was intersected by the tract segment. For each
segment, the binary maps from all 40 subjects were spatially
normalized to the fractional anisotropy map derived from the
average tensor data set, and the maps summed to produce
segment overlap maps on axial, sagittal, and coronal slices (SI
Fig. 3). Finally, a second operator defined ROIs in a randomly
selected sample of 10 subjects, and interrater reliability was
calculated for the long segment (correlation 0.93 for the lateralization
index of the number of streamlines and 0.99 for the
fractional anisotropy measurements).
Statistical Analysis. Statistical analysis was performed by using
SPSS software (SPSS, Chicago, IL). Subjects were clustered into
three groups according to the left-right distribution of the
reconstructed pathways of the direct segment using a k-means
cluster analysis (59). ␹2
(or Fisher’s exact test) was used to assess
the distribution of the lateralization index across the subjects and
between genders. One-sample t test (test value ϭ 0) was used to
assess the lateralization of the index of the fractional anisotropy
values and two-way ANOVA for between-genders differences
(SI Table 1). Correlation analysis was performed between the
lateralization index of the direct segment (streamlines) and the
neuropsychological performances (correlations are significant at
P Ͻ 0.005 after Bonferroni correction for multiple comparisons).
Also, correlation analysis was performed between tract-specific
measurements of fractional anisotropy and neuropsychological
performances (correlations are significant at P Ͻ 0.0016 after
Bonferroni correction for multiple comparisons). ANOVA was
used to account for gender differences in handedness and
neuropsychological performances (SI Table 3). Two-way
ANOVA was used to assess differences in neuropsychological
performances among the three groups. Interrater reliability for
the lateralization index of the long segment was 0.93 for the
number of streamlines and 0.99 for the fractional anisotropy
measurements.
We thank Cathy Price and Marion Cuddy for comments on the manuscript.
This research was funded by the Medical Research Council
(U.K.), the AIMS network, the Wellcome Trust, and the Psychiatry
Research Trust. M.P.G.A. is a Peggy Pollak Research Fellow in Developmental
Psychiatry.
Catani et al. PNAS ͉ October 23, 2007 ͉ vol. 104 ͉ no. 43 ͉ 17167
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17168 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0702116104 Catani et al.